Support Vector Machines and Kernels for Computational Biology
Tutorial by Gunnar Rätsch and Sören Sonnenburg at ISMB/ECCB 2009 in Stockholm, June 28, 2009.
SVMs are very popular in data mining and bioinformatics. This tutorial introduces SVMs and kernel algorithms and illustrates their application to typical problems in computational biology. It covers advances in kernels on strings and graphs, predicting structured outputs and discusses how to derive biological insight from the classifiers.
Note to the participants: Please bring your laptop and make sure it is connected to the internet. We will have some hands-on demonstration requiring either a web-browser or a command line (Linux or Mac).
The tutorial will be structured as follows:
- Introduction to Machine Learning (30 minutes)
- Support Vector Machines and Kernels (30 minutes)
- Kernels for Sequences and Graphs (60 minutes)
- Extracting Insight from the Learned SVM Classifier (30 minutes)
- Structured Output Learning (30 minutes)
- Case Studies (Applications) (30 minutes)
- What is a SVM? by William S. Noble (published in Nature Biotechnology, Volume 24, Number 12, December 2006)
- Kernel methods in genomics and computational biology by Jean-Philippe Vert (in Camps-Valls, G., Rojo-Alvarez, J.-L. and Martinez-Ramon, M. (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing, p.42-63, Idea Group, 2007)
- Support Vector Machines and Kernels for Computational Biology by Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, and Gunnar Rätsch (in PLoS Comput Biol 4(10): e1000173)
We gratefully acknowledge help from Cheng Soon Ong for preparing an earlier version of this tutorial. Moreover, slides were contributed by Peter Gehler, Karsten Borgwardt and Petra Philips.